Abstract
In this paper we present a contextual modeling approach for model-based recommender systems that integrates and exploits both user preferences and contextual signals in a common vector space. Differently to previous work, we conduct a user study acquiring and analyzing a variety of realistic contextual signals associated to user preferences in several domains. Moreover, we report empirical results evaluating our approach in the movie and music domains, which show that enhancing model-based recommender systems with time, location and social companion information improves the accuracy of generated recommendations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Abbar, S., Bouzeghoub, M., Lopez, S.: Context-aware Recommender Systems: A Service-oriented Approach. In: Proceedings of the 3rd International Workshop on Personalized Access, Profile Management, and Context Awareness in Databases (2009)
Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach. ACM Transactions on Information Systems 23, 103–145 (2005)
Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering 17, 734–749 (2005)
Adomavicius, G., Tuzhilin, A.: Context-Aware Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 217–253. Springer (2011)
Baltrunas, L., Ricci, F.: Experimental Evaluation of Context-dependent Collaborative Filtering Using Item Splitting. User Modeling and User-Adapted Interaction (in press)
Breese, J., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)
Burke, R.: Hybrid Web Recommender Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 377–408. Springer, Heidelberg (2007)
Dey, A.K.: Understanding and Using Context. Personal and Ubiquitous Computing 5, 4–7 (2001)
Gorgoglione, M., Panniello, U., Tuzhilin, A.: The Effect of Context-aware Recommendations on Customer Purchasing Behavior and Trust. In: Proceedings of the 5th ACM Conference Recommender Systems, pp. 85–92 (2011)
Karatzoglou, A., Amatriain, X., Baltrunas, L., Oliver, N.: Multiverse Recommendation: N-dimensional Tensor Factorization for Context-aware Collaborative Filtering. In: Proceedings of the 4th ACM Conference on Recommender Systems, pp. 79–86 (2010)
Koren, Y.: Collaborative Filtering with Temporal Dynamics. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 447–456 (2009)
Oku, K., Nakajima, S., Miyazaki, J., Uemura, S.: Context-Aware SVM for Context-Dependent Information Recommendation. In: Proceedings of the 7th International Conference on Mobile Data Management, p. 109 (2006)
Panniello, U., Gorgoglione, M.: Incorporating Context into Recommender Systems: An Empirical Comparison of Context-based Approaches. Electronic Commerce Research 12, 1–30 (2012)
Panniello, U., Gorgoglione, M., Palmisano, C.: Comparing Pre-filtering and Post-filtering Approach in a Collaborative Contextual Recommender System: An Application to E-commerce. In: Di Noia, T., Buccafurri, F. (eds.) EC-Web 2009. LNCS, vol. 5692, pp. 348–359. Springer, Heidelberg (2009)
Panniello, U., Tuzhilin, A.: Experimental Comparison of Pre- vs. Post-filtering Approaches in Context-aware Recommender Systems. In: Proceedings of the 3rd ACM Conference on Recommender Systems, pp. 265–268 (2009)
Yu, Z., Zhou, X., Zhang, D., Chin, C.-Y., Wang, X., Men, J.: Supporting Context-Aware Media Recommendations for Smart Phones. IEEE Pervasive Computing 5, 68–75 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fernández-Tobías, I., Campos, P.G., Cantador, I., Díez, F. (2013). A Contextual Modeling Approach for Model-Based Recommender Systems. In: Bielza, C., et al. Advances in Artificial Intelligence. CAEPIA 2013. Lecture Notes in Computer Science(), vol 8109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40643-0_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-40643-0_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40642-3
Online ISBN: 978-3-642-40643-0
eBook Packages: Computer ScienceComputer Science (R0)